mail-top-senders

name	mail-top-senders
description	Show who sends the most email, communication frequency analysis, and relationship mapping. Use when user asks who emails them most, top contacts, communication patterns, or wants to understand their email social graph. Arguments: optional time range (default: last 90 days), account filter, or "humans only" to exclude automated senders.
disable-model-invocation	false
user-invocable	true
allowed-tools	Bash
metadata	{"openclaw":{"requires":{"bins":"[Truncated]"}}}

Mail Top Senders — Communication Frequency Analysis

Analysis period: $ARGUMENTS (default: last 90 days)

Step 1: All senders ranked by volume

DB="$HOME/Library/Mail/V10/MailData/Envelope Index"
SINCE=$(($(date +%s) - 7776000))  # 90 days

sqlite3 "$DB" "
SELECT a.address, a.comment as name,
       COUNT(*) as total,
       SUM(CASE WHEN m.read=0 THEN 1 ELSE 0 END) as unread,
       MIN(datetime(m.date_received,'unixepoch','localtime')) as first,
       MAX(datetime(m.date_received,'unixepoch','localtime')) as latest
FROM messages m
JOIN addresses a  ON m.sender  = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE m.date_received >= ${SINCE}
  AND m.deleted = 0
  AND mb.url NOT LIKE '%Spam%' AND mb.url NOT LIKE '%Trash%'
  AND mb.url NOT LIKE '%Sent%'
GROUP BY a.address
ORDER BY total DESC
LIMIT 50;" 2>/dev/null

Step 2: Separate humans from automated senders

Use automated_conversation and unsubscribe_type columns (more reliable than address-pattern matching):

• automated_conversation = 0 + unsubscribe_type = 0 → real humans
• automated_conversation = 1 → transactional (Jira, Slack, alerts)
• automated_conversation = 2 OR unsubscribe_type > 0 → bulk/newsletters (noise)

# Human senders only (automated_conversation = 0, no unsubscribe header)
sqlite3 "$DB" "
SELECT a.address, a.comment as name, COUNT(*) as cnt
FROM messages m
JOIN addresses a ON m.sender = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE m.date_received >= ${SINCE}
  AND m.deleted = 0
  AND mb.url NOT LIKE '%Spam%' AND mb.url NOT LIKE '%Sent%'
  AND m.automated_conversation = 0
  AND m.unsubscribe_type = 0
GROUP BY a.address ORDER BY cnt DESC LIMIT 20;" 2>/dev/null

Step 3: Domain-level analysis

sqlite3 "$DB" "
SELECT substr(a.address, instr(a.address,'@')+1) as domain,
       COUNT(*) as cnt
FROM messages m
JOIN addresses a ON m.sender = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE m.date_received >= ${SINCE}
  AND m.deleted = 0
  AND mb.url NOT LIKE '%Spam%'
GROUP BY domain ORDER BY cnt DESC LIMIT 20;" 2>/dev/null

Step 4: Thread participation (two-way communication)

Find senders you also replied to — true relationships vs. one-way communication:

sqlite3 "$DB" "
SELECT a.address, COUNT(*) as received
FROM messages m
JOIN addresses a ON m.sender = a.ROWID
JOIN mailboxes mb ON m.mailbox = mb.ROWID
WHERE m.date_received >= ${SINCE} AND m.deleted=0
  AND mb.url NOT LIKE '%Sent%'
GROUP BY a.address
ORDER BY received DESC LIMIT 30;" 2>/dev/null

Output Format

Top Email Relationships — [PERIOD]

Most Frequent Human Contacts:

Rank	Name	Address	Emails Received	Unread

Top Automated Senders (noise):

Service	Count	Type

Top Domains:

Domain	Count

Insight: note anyone with high unread rate (you receive a lot but don't read → de-prioritize subscription?). Note anyone with very recent last email who you haven't read → potential missed message.